Analysis of empirical mode decomposition-based load and renewable time series forecasting

N. Safari, G. C.D. Price, C. Y. Chung

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

The empirical mode decomposition (EMD) method and its variants have been extensively employed in the load and renewable forecasting literature. Using this multiresolution decomposition, time series (TS) related to the historical load and renewable generation are decomposed into several intrinsic mode functions (IMFs), which are less non-stationary and non-linear. As such, the prediction of the components can theoretically be carried out with notably higher precision. The EMD method is prone to several issues, including modal aliasing and boundary effect problems, but the TS decomposition-based load and renewable generation forecasting literature primarily focuses on comparing the performance of different decomposition approaches from the forecast accuracy standpoint; as a result, these problems have rarely been scrutinized. Underestimating these issues can lead to poor performance of the forecast model in real-time applications. This paper examines these issues and their importance in the model development stage. Using real-world data, EMD-based models are presented, and the impact of the boundary effect is illustrated.

Original languageEnglish
Title of host publication2020 IEEE Electric Power and Energy Conference, EPEC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728164892
DOIs
Publication statusPublished - 9 Nov 2020
Externally publishedYes
Event2020 IEEE Electric Power and Energy Conference, EPEC 2020 - Edmonton, Canada
Duration: 9 Nov 202010 Nov 2020

Publication series

Name2020 IEEE Electric Power and Energy Conference, EPEC 2020

Conference

Conference2020 IEEE Electric Power and Energy Conference, EPEC 2020
Country/TerritoryCanada
CityEdmonton
Period9/11/2010/11/20

Keywords

  • Empirical mode decomposition
  • Load forecasting
  • Time series analysis
  • Wind power forecasting

ASJC Scopus subject areas

  • Information Systems and Management
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering
  • Control and Optimization

Fingerprint

Dive into the research topics of 'Analysis of empirical mode decomposition-based load and renewable time series forecasting'. Together they form a unique fingerprint.

Cite this